Parameter Monitoring in Medical Treatment Systems

ABSTRACT

A blood treatment system with pressure sensors may be configured to control blood flow to and from the patient and use readings of the pressure sensors to determine a change in a pressure drop across a flow restriction in the blood circuit to estimate a condition of the machine or the patient, or outputting data responsive to the estimation. Further embodiments employ measurement of pressure drop to detect abnormal viscosity or viscosity variations in order to detect possible infection.

BACKGROUND

The present invention relates generally to medical treatments, and morespecifically to the control of medical treatments based on the detectionof the properties of fluids involved in treatment.

Due to disease or injury, a patient's renal system may lose sufficientfunction to sustain life. The failure can cause a water imbalance andthe accumulation of toxic elements that are no longer properlyeliminated. For example, end products of nitrogen metabolism (e.g.,urea, creatinine, uric acid, and others) can accumulate in blood andtissues.

There are various forms of renal replacement therapy that can be used totreat renal failure. For example, dialysis removes waste, toxins, andexcess water from the body that would otherwise have been removednormally. Dialysis treatment can be life-saving. Hemodialysis,hemofiltration, and peritoneal dialysis are three types of dialysistherapies generally used to replace renal function.

Hemodialysis removes waste, toxins, and water directly from thepatient's blood by flowing blood and dialysate through an extracorporealcircuit, and exchanging fluids, solutes, and molecular species across amembrane by diffusion and convection. A patient is connected to atreatment machine and the patient's blood is pumped through a bloodcircuit. A patient access using needles or catheters may provide accessto veins and arteries for the supply and return of blood to and from thetreatment machine. The membrane is housed in a dialyzer, a type offilter. As blood passes through the dialyzer, the waste, toxins, andexcess water from the patient's blood are removed and the cleansed bloodis returned to the patient. During a treatment, as much as 90-120 litersof dialysate may be consumed. Treatments may last several hours and maybe performed daily or two to three times per week.

Hemofiltration is similar to hemodialysis but differs in relying more onconvection of fluid from the blood and replacement of the fluid with areplacement fluid. Hemofiltration is better at removing largermolecules. To an extent, convection and diffusion play a role in thefunction of the treatment in both hemodialysis and hemofiltration, andthere are treatments that lie in the middle called hemodiafiltration.

Peritoneal dialysis infuses dialysate into the peritoneum, effectivelyusing the peritoneal membrane as the filter to exchange water anddissolved species with the dialysate. The transfer of waste, toxins, andexcess water from the bloodstream into the dialysate occurs due todiffusion and osmosis during a dwell period. The spent dialysate islater drained to remove the excess water and other materials.

There are a variety of peritoneal dialysis modalities. Automatedperitoneal dialysis includes a drain, fill, and dwell cycle. However, adialysis machine performs multiple cycles of fill and drain on aschedule that includes a dwell interval. This may be done overnightwhile the patient sleeps. With automated peritoneal dialysis, thetreatment machine connects to an implanted catheter and to a source offluid and a drain. The machine pumps spent dialysate from the peritonealcavity, through the catheter, to the drain, and then pumps freshdialysate through the catheter to the peritoneum. A computer controllermay be used to control the machine.

In all renal replacement therapy systems, there is a dire need tomaintain sterility to prevent infection. Also, there is a perennial needfor improved safety because of the risks associated with repeatedtreatments and the fact that the patient's blood and peritoneum are sovulnerable to exposure. Still further, there may be furtheropportunities to extract relevant information about a patient's healthand the efficacy of treatments. Still further, there may existopportunities to use information that is available or which can beextracted readily during treatment in real-time and continuously ordiscontinuously to aid in the management of equipment, patient health,and treatment efficacy.

SUMMARY

The disclosed subject matter includes devices, methods, and systems fordetecting a patient or treatment system status or event, such as adiagnosis of an infection or a patient's fluid status, based onparameters detected using the treatment system. The disclosedembodiments relate to fluid treatment systems or other bodily fluidtreatment systems in which a fluid is removed from a patient. Examplesinclude extracorporeal blood treatment systems and peritoneal dialysissystems. According to embodiments, a property of a fluid from a patientis detected and stored as data over a period of time, for example, overa treatment cycle or over multiple treatment cycles. Data from multipletreatments may be stored and used to extract a prediction of temporalprofiles expected for future treatments. This predicted profile can becompared to a current profile and used to determine if it indicates aremarkable treatment status or patient status, for example, aninfection. Parameters may include temperature and viscosity of the fluidor any other parameter indicative of a physical or chemical property ofthe fluid. In embodiments, the parameter may be proportional toviscosity. These may be absolute parameters or relative parameters—anoutgoing fluid parameter taken relative to an ingoing fluid parameter.Trends in these parameters may be combined with other parametersmeasured otherwise to allow them to be correlated for specific patients.For example, fluid volume status may be correlated with viscosity andhematocrit to create a custom model that can predict one of these givenone or more of the others. In addition, dynamic trends in theseparameters and others may be combined over a treatment or over one ormore days/treatments, the parameters including, for example, pH,hematocrit or hemoglobin level, heart rate, blood pressure, bloodoxygenation, as well as other parameters either directly measured orstored in a data log. Conditions may be automatically changed and timetrends recorded to yield additional diagnostic data. For example, atemporary reduction or halting of ultrafiltration during a treatmentwith time-based sampling of blood viscosity can indicate the rate ofmovement of fluid from the interstitial compartment to the bloodcompartment, and/or indicate whether further ultrafiltration is needed.

It will be observed that the methods, devices, and systems disclosed maybe employed in various combinations to, among other things:

-   -   a. improve the diagnosis of a patient undergoing a treatment,        and in particular, a renal replacement therapy or extracorporeal        blood treatment;    -   b. using single or multiple real-time sensor signals alone or in        combination with logged data, detect correct or incorrect        operation of medical treatment equipment or operator error that        could lead to incorrect operation of medical treatment        equipment;    -   c. improve renal replacement therapy or extracorporeal blood        treatment outcomes; and    -   d. detect patient water content and/or ultrafiltration rate        during a blood treatment continuously or discontinuously and in        real-time.

Objects and advantages of embodiments of the disclosed subject matterwill become apparent from the following description when considered inconjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments will hereinafter be described in detail below with referenceto the accompanying drawings, wherein like reference numerals representlike elements. The accompanying drawings have not necessarily been drawnto scale. Where applicable, some features may not be illustrated toassist in the description of underlying features.

FIG. 1 is a schematic illustration of a peritoneal dialysis orhemodialysis system, according to embodiments of the disclosed subjectmatter.

FIG. 2 is a graph showing the relationship between viscosity andhematocrit level.

FIG. 3 shows a schematic illustration of a feature of a hemodialysissystem, according to embodiments of the disclosed subject matter.

FIG. 4 shows a schematic illustration of a feature of a furtherhemodialysis system, according to embodiments of the disclosed subjectmatter.

FIGS. 5 and 6 show embodiments of systems that automatically divert asample of fluid responsively to an estimation signal indicating thepresence of, or a probability of, a condition of interest such as apatient infection.

FIG. 7A is a figurative illustration of the time-variation of aparameter related to (preferably proportional) to viscosity which mayoccur over a course of a drain cycle of a peritoneal dialysis treatmentfor purposes of discussing statistics that may be used to identify apotential infection, according to embodiments of the disclosed subjectmatter.

FIG. 7B is a figurative frequency domain signal derived from the timevariation of a parameter related to (preferably proportional) toviscosity which may be based on a window function spanning seconds tominutes occur over a course of a drain cycle of a peritoneal dialysistreatment for purposes of discussing features that may be used toidentify a potential infection, according to embodiments of thedisclosed subject matter.

FIG. 8 shows a metric of a parameter related to viscosity over multipletreatments or multiple cycles with each bar 402, 404, 406 showing agiven treatment or a different drain cycle of peritoneal dialysis alldevelop over time in order to illustrate that remarkable changes in theparameter in a given cycle or a given treatment may be used to identifya possible condition such as an infection of the peritoneum.

FIG. 9 is a block diagram of an example computer system according toembodiments of the disclosed subject matter.

FIG. 10 shows a “double pod” article of manufacture that may be used inplace of a single pressure sensor in a fluid circuit such as a bloodcircuit for pressure detection and viscosity-dependent pressure lossmeasurement.

DETAILED DESCRIPTION

A generalized system 100 is shown in FIG. 1. A treatment system 104,which may include a blood treatment device that transfers a fluid, suchas blood, from and to a patient 122. The system may also represent aperitoneal dialysis machine that transfers peritoneal dialysate to thepatient 122 and withdraws peritoneal dialysate from the patient 122.Fluid is conveyed via a fluid circuit 101 that includes lines 112 thatengage, or expose contents to, one or more sensors 109, 111, 108, 110.Sensor signals of sensors 109, 111, 108, 110 are conveyed to acontroller 116 which may control pumps such as pump 102, and other pumpsnot shown, as required. Sensor signals of sensors 109, 111, 108, 110 mayalso be used by controller 116 to display and/or record patientstatus/data, transmit patient status/data to a healthcare provider orany other person, device, or entity (e.g., via paging, email, textmessaging, treatment log, network server), control delivery of amedicine to the patient (e.g., by controlling an IV or a substance addedto the fluid in lines 112).

Fluid may be conveyed through flow restrictions 107, 115 to facilitatemeasurement of a parameter of the fluid, for example, a viscosity of arespective one of blood or peritoneal dialysate in various embodiments.However, any other known means of measuring viscosity may be used inalternative embodiments, such as any known viscometer or rheometer or byusing an optical technique. Captured measurements may be stored by thecontroller 116 on a non-volatile data store 118 that resides locally orin the cloud.

With regard to any of the embodiments disclosed herein, including theclaims, pressure loss through the flow restriction may be used to detecta fluid's viscosity. The calculation may involve the use of an empiricalconversion constant for the flow restriction 107, 115 obtained fromexperiments with the particular fluid and low regime (i.e., turbulent vslaminar) of interest. Some fluids, such as blood, have a viscosity thatvaries with the shear rate so comparable viscosities may be measured atpredefined flow rates or within predefined ranges of flow rates. Thepressure loss for a predefined configuration, flow rate, and fluidproperties (e.g. density, for turbulent flow) other than viscosity maybe identical, or made to be identical by the controller (e.g., flowrate) from one measurement to the next. The controller may control apump or pumps to establish a predefined flow rate stored in a memory bythe controller for purposes of performing a viscosity measurement. Theflow rate may be temporarily established for the purpose of measurementand controlled to other flow rates at other times. Preferably viscosityis measured at Reynolds numbers in the laminar regime (<2100 or <2300)which makes the pressure loss invariant with respect to density. Sinceblood density is proportional to total protein, the water loading of theblood compartment of the patient will be density sensitive. Thus thelaminar flow regime simplifies the measurement of viscosity. As pointedout elsewhere, since for a predefined flow system (restriction 107, 115including the pressure sensors, the restriction for example being astraight length of tubing or a curved tube having a predefined diameter,an orifice, a smooth contraction, or other type of restriction) and apredefined flow rate, the pressure difference indicated by the pressuresensors is simply proportional to viscosity for laminar flow, thresholdsfor response can be established in terms of pressure drop. In otherwords it is not necessary to convert pressure drop to viscosity becausethe latter differs from pressure drop only by a constant ofproportionality. To extend the embodiments to turbulent flow (Re>2000)the pressure loss depends on density. The above features are well-knownfrom empirical equations for calculating pressure drop in fluid flowfixtures, for example, Darcy Weisbach equation and Hagen-Poiseuilleequation.

The controller 116 may or may not have control functions such asoperating the pump 102 or pumps and may be embodied in a stand-aloneprocessor. Alternatively, controller 116 may control the treatmentsystem 104 and/or the pump 102, in order to take an action responsivelyto a detected status or to enhance the acquisition of data relating tothe patient or treatment being administered.

In embodiments, the one or more sensors 109, 111, 108, 110 may includeone or more pressure sensors or pressure pods, blood oxygen levelsensors, pulse sensors (which may be combined as pulse-oximeter),temperature sensors, pH sensors, or other sensor types that measure aphysical, chemical, or other parameter of a fluid. System 104 mayalternatively or additionally include a weight scale 113 that provides asignal indicative of the weight of the patient 122, and the controller116 may use such signal, instead of or in combination with signalsproduced by the one or more sensors 109, 111, 108, 110, to implement anyfunctionality described herein with reference to a treatment of thepatient 122. As indicated elsewhere, the flow restriction 107, 115 mayinclude tubing or all or part of the pressure sensors 109, 111, 108,110.

In some alternative or additional embodiments, system 100 may transfer afluid other than blood or peritoneal dialysate from and to the patient122, such as urine, lymph, cerebrospinal fluid, amniotic fluid, synovialfluid, etc. In these embodiments, sensors 109, 111, 108, 110 may measureany physical, chemical, or other property of the fluid, and thus may beused by controller 116, alone or in combination with other sensor datafrom any other sensors, to display and/or record patient status/data,transmit patient status/data to a healthcare provider or any otherentity (e.g., via paging, email, text messaging, etc.), control deliveryof a medicine to the patient (e.g., by controlling an IV or a substanceadded to the fluid in lines 112), etc. Again, the fluid may be conveyedthrough flow restrictions 107, 115 to facilitate measurement ofviscosity. However, any other known means of measuring viscosity may beused in alternative embodiments.

As indicated, certain embodiments relate to peritoneal dialysistreatments. As discussed in United States Patent ApplicationUS20140018727 (incorporated by reference in its entirety herein), aperitoneal fill/drain line may include a distal fluid pressure sensorlocated near the patient connection and a fluid pressure sensor near theinlet to a peritoneal dialysis (PD) cycler. The fluid path between thetwo sensors creates a restriction such that when fluid is pumped to orfrom the patient, a pressure differential is produced that indicates thefluid viscosity. The viscosity can indicate various processes that mayrequire intervention, including disease, fluid level, or other factorsthat may affect treatment or patient health.

Embodiments provide improvements on this art. In embodiments, bymeasuring pressure drop across elements of a fluid circuit that arealready in place for treatment, the cost of consumables is minimized.For example pressure sensors may be present for other purpose such asdetection of arterial or venous pressure, out-of-bounds operatingconditions, for pump inlet pressure compensation for accurate flowconversion from pump speed to flow rate base on pump curves, or otherpurposes, if separated by a flow path that can function as a flowrestriction (all flow paths can but preferably such a flow path provideslaminar flow) and the only additional components needed are those thatare required for detecting pressure. Such pressure sensors may thusalready define a structure such as discussed above with respect topressure sensor 109 and pressure sensor 111 linked by a flow restriction107 (similarly for pressure sensor 108 and 110 linked by flowrestriction 115). In other embodiments, the existence of a pressuresensor and a flow path that provides resistance may be supplemented by asingle additional pressure sensor to provide the components needed formeasurement of viscosity. In either case, a controllable pump may alsobe in place in the treatment system for performing treatment.

Thus, in embodiments, the pressure sensor or sensors may be pressuresensors that perform functions other than viscosity measurement. Theline through which pressure drop is measured may include a blood line.The viscosity may be used alone or in combination with other data togenerate parameters for detecting a condition of the treatment or thepatient. In embodiments, using pressure pods allows a separation of thetransducer, which can be permanent and connected to the treatmentmachine, and allows the placement of the pod anywhere on the circuit.

Examples of other purposes for which the pressure sensors of theembodiments may be used include (1) testing line or connection integrityby pressure-decay testing, (2) peristaltic pump flow control based oninlet pressure compensation, (3) pressure monitoring for out-of-boundconditions, (4) blood or peritoneal cavity pressure measurement. In suchcases, the pressure sensors may also provide for viscosity detection andfor other functions as described herein.

Using A Flow Restriction

The pressure sensor arrangement of FIGS. 2A, 3A, 3B, 4A, 4B, 6A through6K of US20140018727 and other embodiments provide proximal and distalpressure sensors which are used to measure pressure drop along the PDfill/drain line. Such arrangement of the PD fill/drain line, includingthe amount and degree of curvature of the flow path, whether it ispinched in places or not, can vary from treatment to treatment.

In some embodiments, to avoid problems with using the fill/drain line(or separate fill and drain lines, if present), a flow restriction thatis resistant to variation from one treatment session or tubing set toanother may be provided. Pressure detection through this repeatable flowrestriction is used to obtain pressure drop data that may be used todetect viscosity or variations thereof (e.g., hemoglobin or hematocrit)as described herein. A rigid tube length with low manufacturingtolerance (lower than other portions of the fluid circuit) may beprovided. In embodiments, a single housing with two pressure pods joinedby a rigid injection-molded port or tube length may be provided, thelatter defining a flow restriction (e.g., 107, 115). Such a “double pod”may be used in any fluid circuit having a portion where a pressuremeasurement is required (e.g., pump inlet pressure compensation, venouspressure, or arterial pressure). FIG. 10 shows a “double pod” 502article of manufacture that may be used in place of a single pressuresensor in a fluid circuit such as a blood circuit for pressure detectionand viscosity-dependent pressure loss measurement. A first pressure pod506 is joined to a second pressure pod 504 by a flow path element 510which defines a restriction as discussed elsewhere in the presentdisclosure. These elements may be formed of a single housing. Air sensorlines 508 and 510 connected to pressure transducers. Fluid, such asblood, flows through ports connected to inlet and outlet lines 512 and514.

In embodiments, examples of flow restrictors which may be added to thoseof FIGS. 2A, 3A, 3B, 4A, 4B, 6A through 6K of US20140018727, herebyincorporated by reference as if fully set forth herein, or asalternatives thereto, include an orifice and a rigid channel. Pressuresensors may be arranged on either end of the flow restriction. One orboth of the pressure sensors that measure the pressure drop may includeany pressure sensor known in the art such as any of those shown in theFIGS. 2A, 3A, 3B, 4A, 4B, 6A through 6K of US20140018727. Inembodiments, the flow area of the fill/drain line between the flowrestriction and the pressure sensor may be large enough such that theflow restriction presents a much greater restriction to flow than therest of the fill/drain line. With such a configuration, variability inthe arrangement of the PD fill/drain line, inner diameter, the amountand degree of curvature of the flow path, whether it is pinched inplaces or not, for example, may introduce a minor and tolerablevariation in the viscosity calculated from the pressure drop. Inembodiments, the flow restriction may be sized to create a pressure dropthat is a predefined number of times greater that the greatest typicalvariation created by variations in the above parameters.

Indicating A Trend in Viscosity

In embodiments, as an additional feature that may be provided to improvea function to detect conditions indicated by viscosity changes andmagnitudes, the system may capture and analyze historical trends ofviscosity (and optionally combine historical trends with other data asdiscussed later). In embodiments, the pressure drop data is recordedduring a treatment, including, optionally at least, at multiple timesduring a single treatment to create a record over time of the pressuredrop data. These pressure data may be used with predefined configurationdata stored as a model or with empirically-derived data to generate atrend or instance of viscosity over time during a treatment. Asindicated, the pressure drop can be used without conversion, inembodiments. The trend or instant (or statistic derived from a trend)may be stored in a patient profile and referred to during each treatmentfor comparison by a microprocessor. In embodiments, when a viscosityassociated with a current treatment is different according to apredefined characteristic from a predefined prediction of viscositybased on historical pattern, an indication of the departure may begenerated and output to a user interface, a physician, a technician, atreatment log, a nurse, a nurse station, or any other receiving personor device. Further, such data may generate a response in the PD systemsuch as a warning or shutdown.

Still another alternative or additional embodiment monitors the pressuredrop caused by flowing blood in an extracorporeal blood treatment systemsuch as a hemodialysis system. Using a similar system attached tomeasure pressure loss in a blood line or flow restrictor, the change inthe blood viscosity can be monitored, predictions based on historicaltrends generated, and so on as discussed above. Since blood isnon-Newtonian (shear-thinning), a viscosity description may be generatedand stored for comparison to measured conditions during a treatment. Forexample, an empirical prediction model such as: a normalizedstress/strain profile or, assuming a controlled flow geometry, pressureloss/mass flow profile, may be generated from historical pressure datafor comparison to instant conditions during a treatment. The predictionof pressure drops (or shear) may be further refined based on hematocritmeasured using a sensor or stored data. In alternatives, viscosity, orpressure-drop alone, may be detected and compared to thresholds forpredefined flow geometry (between the pressure sensors) and conditionssuch as flow rate and blood temperature. In other embodiments, theviscosity may be measured at multiple flow rates and compared asindependent parameters or the viscosity at the multiple flow rates maybe averaged. Instead of viscosity, the pressure drop divided by flowrate may be used for laminar flow.

In embodiments, blood viscosity data are collected in combination withblood flow rate data to indicate the instant blood flow rate at eachviscosity measurement. Alternatively, viscosity (or pressure drop) ismeasured at a predefined flow rate. In embodiments, blood viscositymeasurements are performed at controlled blood flow rate levels. Forexample, embodiments first reduces/increases the blood flow rate inlines 112 to reach a pre-defined level and then measures blood viscosityat the pre-defined blood flow rate level. Embodiments repeats bloodviscosity measurements to obtain a reliable measurement, for example, bydiscarding outlier measurements, by averaging all or a subset of themeasurements.

During PD therapy, there is a drain phase in which used dialysate(effluent) from the patient is pumped to drain. This phase typicallyincludes a period during which fluid is removed from the patient at afixed rate (typically between 200 and 400 mL/min). For a fixed pumpingrate, the pressure drop between the distal pressure sensor and the pumpinlet sensor is proportional to the viscosity. The cycler can measurethe pressure at these two sensors (at a suitably high sampling rate) andcalculate an average pressure drop, which can be converted to a valuethat is proportional to the effluent's viscosity.

By recording the average pressure drop during certain portions of eachdrain cycle, a profile can be generated for a given patient thatreflects his or her typical effluent viscosity for each drain cycle.Over a period of time, this data can be used to statisticallycharacterize that patient's “viscosity as a function of drain cycle”profile.

The values computed for a given day's treatment can be communicated to arelevant health care professional (HCP) as part of a daily treatmentrecord, along with a comparison to the patient's average “viscosity vs.drain cycle” profile or prediction.

If, on a given day or at a given time, there is a significant deviation(indicated by a change beyond a predefined range stored in thecontroller) in the patient's viscosity, that fact can be highlighted bythe controller in the output to the patient's daily treatment record,signaling to the HCP that there has been a significant change indicatedby a detected difference from a predefined threshold. Significantchanges in effluent viscosity can be triggered by the onset ofinfection, hence this can provide HCP's with an early indication ofinfection, allowing for rapid intervention/mitigation. In embodiments, asignificant change is a change that is beyond a predefined magnitude. Inembodiments, the predefined magnitude is determined based upon astatistics derived from viscosity or other measurements in a group ofpatients or from historical viscosity or other measurements of a singlepatient.

During hemodialysis (HD) therapy, a similar approach can be used duringthe course of a treatment to monitor changes in the viscosity of apatient's blood during the course of treatment. Generally, bloodviscosity varies with hematocrit levels. When pumping blood, any pair ofinline pressure sensors may detect pressure differentials across a flowrestriction such as an orifice or length of tubing, while pumping; thesepressure differentials will be proportional to the blood's viscosity,and hence, to the hematocrit concentration. Normal values for Hematocritare known in the art, for example, as follows:

Renal patients may have abnormal, usually low, hematocrit. This is oftentreated with medication. Even normal patients vary, but in general thereis a trend as follows:

-   -   Male: 40.7 to 50.3%    -   Female: 36.1 to 44.3        Normal results for children vary, but in general are as follows.    -   Newborn: 45 to 61%    -   Infant: 32 to 42%

FIG. 2 shows an example graph of a relationship between viscosity andhematocrit compared to the Einstein theoretical model for a dilutesuspension. It shows that the viscosity is more sensitive to theconcentration of blood cells than would be predicted by the model.Patient treatment logs or other output such as on a user interface mayinclude the output of hematocrit or hemoglobin based on a conversionfrom the viscosity or pressure drop data. The hematocrit or hemoglobinmay be compared to predefined ranges stored by the controller toindicate abnormal conditions which may be highlighted in the output dataor cause the controller to generate a corresponding indication.

Indicating Whether Dry Weight Has Been Reached

In embodiments, by monitoring changes in blood viscosity (and hence,hematocrit concentration) during the course of an HD treatment, atreatment system or connected digital monitoring system determines whena patient has reached their dry weight, and adjust the therapy such thatthe rate at which ultrafiltration occurs is appropriately related to therate at which the patient's body is able to replace blood fluid volume.Dry weight indicates a normal level of water in the patient. It may becharacterized by reference to the patient's weight when the normal fluidlevels are achieved or by the normal concentration of bloodconstituents. Ideally, a patient should leave a dialysis treatment athis or her dry weight. An increase in viscosity above this “plateau” canprovide an early indication that the patient might be about toexperience a hypotensive episode or “crash” due to low blood fluidvolume.

When using viscosity measurement of the blood during an extracorporealtreatment that also includes fluid removal from the patient (i.e.,ultrafiltration or UF), besides continuously monitoring the rate ofchange in hematocrit for values that exceed predefined limits asdescribed above, the UF can be reduced or completely halted for a briefperiod of time to allow the cellular/interstitial fluid volume toequilibrate with the blood. If the patient's measured pressure drop,hematocrit, and/or viscosity does not change (or shows a change smallerthan a predefined range) during the reduced/halted UF period, thepatient may be determined by the controller to be at the patient's dryweight. More specifically, the controller 116 may store multiple samplesof pressure drop information (or the pressure data reduced to viscosityor hematocrit according to a stored formula or lookup table) todetermine a trend in viscosity and/or hematocrit during thereduced/halted UF period. If the patient's measured indicator fallsduring the reduced/halted UF period, the patient may be determined tohave not reached their dry weight, and therefore more fluid can beremoved. The amount of this “rebound” of viscosity/hematocrit can beused by an automated controller to cause a change in the UF rateincluding halting UF altogether. If the viscosity/hematocrit does notchange during the reduced/halted UF period, the controller 116 maygenerate an indicator that the patient may have reached their dry weightand it may cease further ultrafiltration as well as indicate thedetected condition via the user interface 130. This effect, calledrebound, is detected indirectly using pressure drop, viscosity, and/orhematocrit. The magnitude of this rebound in hematocrit indicated byviscosity can also be used by the controller 116 to alter the rate of UFas well. For example, the UF rate may be controlled by the controller116 such that the rate is constant and such that the patient reachestheir dry weight at the end of a scheduled termination of treatment orat a predefined time before a scheduled end of treatment.

Referring to FIG. 3, a blood circuit 141 has a venous line 140 and anarterial line 142 connectable to a patient 122. A pump 132 pumps bloodthrough the arterial line 142, through a treatment device 130, throughthe venous line 140 back to the patient 122. The arterial line 142 has aviscosity detector 138 that includes an upstream pressure sensor 144 anda downstream pressure sensor 148 with a flow restriction 146 betweenthem. Similarly, the venous line 140 has a viscosity detector 136 thatincludes an upstream pressure sensor 144 and a downstream pressuresensor 148 with a flow restriction 146 between them. The flowrestrictors 146 may be a tube with a predefined shape and innerdiameter, a precisely sized port with a shaped inlet and outlet or anyother suitable device for generating a viscosity—indicating pressurechange when fluid flows through it.

The configuration of FIG. 3 provides an arrangement in which theviscosity of blood may be determined at the entry of the treatmentdevice 130 and at the exit of the treatment device 130 so that theviscosity and/or derived hematocrit properties in and out of thetreatment device may be compared by the controller 116. The viscositymay be used by the controller as an indicator of a change in hematocritby converting the viscosity to a hematocrit using a function such asrepresented by FIG. 2. The hematocrit level can be converted to anestimate of the water content of the blood. The water content levelsentering and leaving the treatment device 130 can provide an independentestimate of an instantaneous ultrafiltration rate. The latter may benumerically cumulated over time to estimate the total fluid gained orlost during a treatment.

Estimating Ultrafiltration Rate

Many extracorporeal blood treatments, such as renal replacement therapy,remove water as part of their treatment function. In embodiments, bycalculating the instantaneous ultrafiltration rate, the controller 116may be enabled to control the rate, for example to limit it to apredefined rate and no higher or to limit the cumulated fluid gained orlost according to a prescription. As for the rate of fluid removal orultrafiltration rate, it may be advantageous to limit this parameter fora number of reasons, including to limit the risk of forming clots in thefluid circuit including the treatment device and/or to limit theimbalance between the patient water level in the blood and interstitialcompartments. To draw water at a desired rate and also provide a precisecontrol of the total volume withdrawn during a treatment, extracorporealblood treatment machines are required to maintain precise control of thedifference between the volume of arterial blood drawn from a patient andthe volume of venous blood returned to the patient. Balancing mechanismsare known in the art for this purpose, but none are perfect. Balancingmechanisms may generate an independent estimate of ultrafiltration ratebased on a pump speed, fluid weight, flow rate, or other mechanism,depending on the type of balancing mechanism and according to knownprinciples.

However, in embodiments, such estimations may be combined withultrafiltration rate estimated as described herein (e.g., based ondirect hematocrit measurement or based indirect hematocrit measurementas a function of viscosity—or other) to detect a treatment error or toverify or improve estimates, for example by averaging the twoestimations and controlling ultrafiltration responsively to the average.The ability to monitor hematocrit continuously provides an additionalway of control and insurance against flawed functioning of the fluidbalance system of the extracorporeal treatment system.

In embodiments, the controller 116 may compare the hematocrits of bloodentering and leaving the blood treatment device 130 and compare to anestimate of the fluid balancing component used to determine theultrafiltration rate. If the estimates of these two subsystems vary by apredetermined amount, the controller 116 may output an indicator of thevariance. This indicator may be used by the controller to shut down anongoing treatment automatically and/or create a signal to an operator.

FIG. 4 illustrates an embodiment which is the same as FIG. 3 except thatthe system 136 includes two blood pumps, a venous blood pump 133 and anarterial blood pump 132. The volume per unit time pumped by each pumpdetermines the net rate of fluid removal from the patient 122. It willbe evident to the skilled person that a variety of mechanisms areavailable in the art for controlling, under control of the controller116, the net rate of fluid removal and that the example shown is onlyone of these. The system of FIG. 4 may be used to allow the controllerto select a current rate of UF at any point during a treatment and tovary it according to a selected control scheme. This function may beused to provide a reduction or pause in UF during treatment to allow themeasurement and recording of a trend in viscosity over time or a trendin hematocrit by deriving and recording the same from multiple viscositymeasurements over time. The rebound of hematocrit after a period of UFis known to be an indicator of whether it is safe to remove additionalfluid from a patient.

The present system allows this determination to be made automaticallyduring a treatment and at multiple times by building into theoperational profile a series of positive UF intervals with UFreductions/pauses between them. The controller 116 may generate anindicator of a rebound magnitude and rate, for example a graph, and usethat to make an automatic determination of whether to halt or reduce therate of UF or to increase it. The indicator may also be displayed on auser interface.

Note that although embodiments described detect viscosity by using aflow restrictor and pressure sensors, through which all of the bloodbeing treated flows, there are other mechanisms for continuouslysampling viscosity. For example, microfluidic samples can be removedfrom a flow stream at intervals and applied to a viscometer that detectsviscosity and generates a signal thereby. Destructive testing can beused where they rely on very small sample sizes. A sampling event may betriggered by a command to test the blood viscosity or the event may betriggered automatically by the controller 116. There are a variety oftechnologies suitable for measuring blood viscosity, and pressure dropacross a restriction (such as a capillary) is only one.

As mentioned above, the pressure sensors used to detect viscosity (andchanges thereto) can be arranged in various ways. In embodiments, forexample, two sensors may be placed in series in the arterial bloodline,post blood pump, pre-dialyzer, in examples, separated by a molded tubewhich may act as a flow restrictor with appropriate properties for thepurpose. Another embodiment may use a patient arterial blood line as aflow restrictor in a manner that is similar to the use of the fill/drainline described for PD.

Using Data Profiles And Classifiers

For PD treatments, the use of data to create an average “viscosityversus drain cycle” profile for a given patient, and comparing a givenday's data to that average to flag significant deviations, may providean early indicator of infection (peritonitis) or other complicationsrelated to PD therapy such as bleeding.

In HD treatments, data may be developed during treatment to establish abaseline viscosity/hematocrit level. Later, this baseline data may becompared to subsequent treatments to detect changes.Viscosity/hematocrit may be monitored throughout a treatment, and atrend therein over the course of a current treatment may be compared totrends, or an average trend (baseline average trend or baseline trends),during previous treatments of the same patient, multiple patients, or acorresponding class of patients.

A baseline temporal profile of viscosity/hematocrit can take the form ofa viscosity/hematocrit level and/or an error range, both of which may bedeveloped using statistics cumulated over multiple treatments over aperiod of time. In addition to, or as an alternative to, a temporalcurve of mean or error band, other statistics may be generated. Forexample, a worst-case rate of change of viscosity may be developed usinga patient's previous history, and data responsive to the worst-casechange (over time and during a single treatment) in viscosity can becompared to a current temporal profile of viscosity versus time. Otherfeatures of a curve can also be extracted and generalized, including theparameters of fits to functions (e.g., orthogonal series, power series,least squares fit to other functions such a Gaussians) may be used torepresent both a current evolving time series of viscosity/hematocritmeasurements and a baseline.

The baseline parameters may be derived from the treatment of aparticular patient so that each establishes a custom individualbaseline. Alternatively, patients can be classified and a baseline canbe shared among the patients in a respective class. The temporal profileof viscosity/hematocrit over time may define a multi-dimensional featurespace that can be applied to a classifier to diagnose various conditionsincluding a pinched line, hypovolemia, low HCT, high HCT, a disease, acorrect or incorrect dialysate composition, and other conditions.Generally, a classification is a problem in machine learning andstatistics that aims to classify an observation as a member of a numberof known classes. A classifier may be trained given a set of knownclasses and a set of observations with known class memberships. Inembodiments, the feature space may be augmented with other inputs suchas patient weight, measured HCT, cardiac rate, blood oxygen level,respiration rate, galvanic skin resistance, core temperature, skintemperature, peripheral temperature, measured patient activity level,and/or other parameters. Measured patient activity level may be detectedby accelerometers or force sensors in the patient's bed or chair, avideo stream classifier receiving input from a live view of thepatient's body, detected Internet activity attributed to a patient'sInternet-connected terminal, detected telephone usage, a classifierreceiving input from a live audio stream from a microphone in thevicinity of the patient, and/or other parameters.

In the above embodiments, a viscosity of a fluid from a patient ismonitored directly or indirectly over a period of time during atreatment. All of the above embodiments may be modified to monitor anduse temperature of the fluid in the same way. According to suchembodiments, temporal temperature profiles may be stored as curves foreach patient and compared to a current trend of temperature during atreatment. Other conditions of the treatment such as the temperature ofthe fluid going to the patient may also be combined with the temperatureof fluid being withdrawn from the patient and/or with the fluid massflow rate to generate a heat loss or gain output to the fluid. Thesetemperature-based parameters may be used in the same way as discussedwith respect to viscosity to classify a condition of the treatment or acondition of the patient. For example, a patient with a fever may outputmore heat than a patient that is afebrile.

In the above embodiments, a viscosity or temperature of a fluid from apatient is monitored over a period of time during a treatment. All ofthe above embodiments may be modified to monitor and use temperature ofthe fluid, in the same way, in combination with the viscosity. Accordingto such embodiments, temporal temperature profiles and viscosityprofiles may be stored as curves for each patient and compared to acurrent trend of temperature and/or viscosity during a treatment. Otherconditions of the treatment such as the temperature and viscosity of thefluid going to the patient may also be combined with the temperature andviscosity of fluid being withdrawn from the patient and/or with thefluid mass flow rate to generate a change in the respective parameterincluding heat loss or gain output to the fluid. These combinedparameters may be used in the same way as discussed with respect toviscosity to classify a condition of the treatment or a condition of thepatient. For example, a patient with a fever may output more heat than apatient that is afebrile. By combining a classification signalindicating a fever with a classification signal indicating a change inviscosity, both generated relative to a baseline expectation, theclassification estimate's reliability can be increased. For example, ifboth temperature and viscosity tend to indicate an infection, theclassification by the controller of the existence of an infection ismore reliable.

In any of the embodiments above, the viscosity and/or temperature flowto the patient, as well as returning from the patient, may be monitoredto determine a change in the respective property which may be stored inaddition to the respective property or instead of the respectiveproperty. The stored baseline data representing predicted parameters maybe attended by, or replaced by, stored baseline data for prediction ofthe respective change from patient-ingoing to patient-outgoing fluidparameters.

The mapping of measured changes in viscosity, along with otherparameters, to conditions, may be generated using various methodsincluding artificial intelligence techniques. Examples includesupervised and unsupervised learning techniques to generate classifiersincluding naive Bayesian classifiers, self-organizing maps, and neuralnetworks.

Example Implementation

One example embodiment implements a fill/drain line for PD or a bloodline for extracorporeal blood treatment. The example embodiment includesa 12 ft. PD fill/drain line created from tubing—Teknor Apex MD-50263thermoplastic elastomer, 4.0 mm×7.0 mm ID/OD. In the example embodiment,non-contacting pressure sensors are installed at the beginning and endof this line to measure pressure drop and calculate viscosity from thepressure drop and flow rate based on empirical or theoretical data. Inrespective embodiments, the pressure sensors may be located on eitherside of a flow restrictor, which may be a length of the fill/drain lineor some flow restriction element in the fill/drain line.

In any of the foregoing embodiments, a treatment system may becontrolled to modify the rate of flow of fluid, or to halt the flow offluid for an interval, in order to increase an effect such as heattransfer to the fluid or the recruitment of viscosity changing elementsinto the fluid from the patient's body. This may increase the propertysignal and make machine classification diagnosis more reliable. Inembodiments, the controller may first make a classification based onunmodified flow rates or flow regimens (e.g., in the absence of anyhalting of the flow), and then in response to a weak diagnosticreliability estimate of the classification, modify the rate of flow offluid or halt the flow of fluid for an interval, thereby enhancing theparameter change that is monitored.

In any of the foregoing embodiments, hematocrit can be measured directlyusing an optical sensor. Optical sensor technologies capable ofmeasuring the volume concentration of red blood cells are known. Forexample, they may employ optical coherence tomography and may detectabsolute magnitudes in a fluid line carrying blood or changes in thebody by means of a low-penetration sensor applied to the skin. Suchindependent indications of hematocrit level may be combined with thosedescribed above calculated from viscosity or used as an alternativethereto. The hematocrit may thus be measured at the inlet and outlet ofthe treatment device and an ultrafiltration rate may be calculated fromthese. More traditional hematocrit measurement methods may be employedas well, for example, automatically extracting a small sample forcentrifugation and measurement of a packed cell volume therefrom.

In any of the foregoing embodiments, the personal profile data of apatient may be stored as a fit to predictive model of apatient—treatment device system. In such embodiments, the predictivemodel may be adapted to account for the impact of the flow rate orultrafiltration rate modification. The predictive model may permit theestimation of an ideal ultrafiltration and flow rate or any othertreatment parameter. The predictive model may receive, as parameters,additional data relating to the patient, such as weight, body massindex, sensitivity to pyrogens, body fat, circulatory health, peritoneumhealth or indicia such as PD treatment history, etc. These additionaldata may be parameters of the model of the patient that are used tocustomize the model for the patient, thereby improving the accuracy ofclassification based on the model.

Fluid Sampling

Any of the foregoing embodiments may include a mechanism forconditionally capturing fluid for further testing in response to anestimation, during a treatment, of a notable condition such as patientinfection. FIGS. 5 and 6 show embodiments of systems 300A and 300B,respectively, that automatically divert a sample of fluid responsivelyto an estimation signal indicating an existing or possible condition ofinterest such as a patient infection. A treatment machine 300 may be arenal replacement therapy machine such as a hemodialysis, a hemofilter,a peritoneal dialysis machine, or any other type of machine foradministration of medical treatments. Respective fluid circuits 301 or303 include one or more lines including inlet and outlet lines 304 and302, respectively. The treatment machine may include sensors and pumps(not shown) effective for providing and regulating a flow of fluid suchas blood or peritoneal dialysate through the one or more lines. A flowdiverter 306 is controlled by the treatment machine 300 as indicated bya dashed line 312. In the system 300A, a sample container 308 receives asample of fluid in response to an estimate of a condition by thetreatment machine 300. For example, the treatment machine (e.g., aninternal controller on the treatment machine) may detect an infection asdiscussed above in response to a viscosity or temperature or othersignals. In response, the flow diverter may be activated to generate asample of fluid which is stored in the container 308. The flow diverter306 may be combined in embodiments with the container 308. For example,the combination may be embodied in a syringe with a passive seal that isopened when the syringe is activated to draw a sample. The fluid may beblood, spent dialysate, or any other fluid described herein. In thesystem of FIG. 300B, the sample container 318 that receives the sampleof fluid through a sample connection 316 is part of a fluid pack 322which may include fresh dialysate concentrate or dry solute used for thepreparation of dialysate.

FIG. 7A is a figurative illustration of the time-variation of aparameter 398 related to (preferably proportional) to viscosity of aspent peritoneal dialysate solution during a drain cycle of a cyclerassisted treatment or other treatment. The parameter may be viscosity,proportional to viscosity or otherwise depend on viscosity, for example,pressure drop across a flow restriction. The flow restriction andpressure sensors may be structurally as identified with any of thedisclosed embodiments, including blood circuit embodiments. Thetime-resolved signal may be samples from an A/D converter stored in acontroller. The samples may represent a moving average or raw samples.The parameter may be derived from pressure signals. The pressure signalsmay be low-pass filtered (analog or digital). The parameter may be asample or multiple samples at a predefined flow rate to minimize errorwhen comparing samples taken at different times over a course of a draincycle of a peritoneal dialysis treatment. The controller of theperitoneal dialysis cycler may convert the samples to a statistic andapply the statistic to an algorithm that indicates a potential of aninfection. Examples of statistics may be the max value of the parameter,the average, a max value of a moving average defined by a selectedaveraging kernel, a measure of variability such as standard deviation orvariance. Other statistics may be generated as well.

Because of a lack of mixing of the contents of the peritoneal cavityduring the dwell phase, the viscosity of the spent dialysate residing atdifferent regions of the peritoneal cavity such that as the spentdialysate is pumped out, corresponding variations in the time-resolvedviscosity may appear. Even where there is little spatial variation inthe viscosity of the spent fluid at the time of or just before draining,the maximum value and average statistics may reveal a magnitude ofviscosity is indicative of infection. Thus, a controller may storethreshold values for any of the statistics or others which may becompared to the statistic derived from the time-based parameter. Astatistic whose value exceeds the threshold may cause the controller togenerate a signal indicating a probability of an infection that isoutput by the controller. Multiple thresholds may be defined eachcorresponding to a probability as indicated by a table stored in thecontroller.

In response to the signal indicating a probability of an infection, thecontroller may output instructions for taking a sample of mayautomatically divert a sample to a container as taught in the presentdisclosure. Any of the other responses to a signal indicating a risk ofinfection discussed elsewhere herein may be taken by the controller,including outputting a message on a display of a user interface, storingan indication in a treatment log, reporting the result to a remotephysician via a text message, email, telephone message or equivalent.

FIG. 7B is a figurative frequency domain signal derived from the timevariation of a parameter related to (preferably proportional) toviscosity which may be based on a window function spanning seconds tominutes occur over a course of a drain cycle of a peritoneal dialysistreatment. The benefit of identifying a fluctuation in viscosity thatmay be revealed by such a filter (PSD stands for power spectral densityin the figure), for example observing a peak 399 in a predefinedfrequency range, is that it can reveal inhomogeneous concentrations ofthicker fluid or lumpiness in the spent peritoneal dialysate. While asimilar capability may be attributed to a statistic based on thetime-varying signal, such a filter may provide a more well-definedsignature that is characteristic of certain infections. Of course, apressure difference signal can be bans pass or high pass filtered toobtain a similar output.

FIG. 8 shows a metric of a parameter related to viscosity over multipleperitoneal dialysis treatments or multiple cycles of a single peritonealdialysis treatment. Each bar 402, 404, 406 illustrates the value of theparameter, or a statistic thereof, for the given treatment of multipletreatments or for the different drain cycle of a single treatment. Thecontroller may store these values with respect to time in order toidentify any remarkable changes in the parameter in a given cyclerelative to multiple cycles or for a given treatment of multipletreatments. The significant change (indicated by a change beyond apredefined threshold stored by the controller) may be used to identify apossible condition such as an infection of the peritoneum. Here then,the relative value of the parameter is the indicator rather than theabsolute value alone. The relative change detection may be combined withthe absolute value to provide an additional indicator. For example thethreshold for a relative change may depend on the absolute magnitude ofthe parameter value.

In any of the foregoing and following embodiments, including the claims,where viscosity is identified as a parameter of interest, the viscositymay be replaced by a parameter that is related to it, for exampleproportional to viscosity with corresponding adjustments to any relatedthresholds. For example, rather than detecting viscosity, in anyembodiment, a pressure difference may be detected and employed withoutfurther modification or reduction. It should be readily apparent thatsuch embodiments may function in the same manner as described forembodiments where viscosity is directly detected or derived from asensor signal such as a pressure difference signal. Also in anyembodiment, including the claims, the parameter may be compared topredefined thresholds for a given flow rate or normalized against flowrate.

According to embodiments, the disclosed subject matter includes a fluidtreatment system with a machine that has one or more pumps and acontroller. The machine may include further actuators and sensors andmay be adapted for administration of a medical treatment. A fluidcircuit is in engagement with the one or more pumps and having inlet andoutlet lines for conveying a fluid to and from a patient respectively. Aviscosity sensor is present in at least the outlet line. The viscositysensor generates a viscosity signal indicative of viscosity of fluid inthe at least the outlet line. The controller is programmed to sample, atmultiple times, the viscosity signal and store parameter data responsivethereto. The controller is further programmed to control the flow offluid in the inlet and outlet lines responsively to the stored parameterdata.

The embodiments further include variations of the foregoing embodimentsin which the viscosity sensor in at least the outlet line is a viscositysensor in the inlet and outlet lines. The embodiments further includevariations of the foregoing embodiments in which the inlet line andoutlet lines are blood lines. The embodiments further include variationsof the foregoing embodiments in which the inlet line and outlet linesare peritoneal dialysate lines. The embodiments further includevariations of the foregoing embodiments in which the inlet and outletlines are blood lines connected to an inlet and outlet, respectively, ofa treatment device.

The embodiments further include variations of the foregoing embodimentsin which the machine is adapted to control a balance of fluid thepatient by regulating a ratio of total volume of fluid removed from thepatient to total volume supplied to the patient.

The embodiments further include variations of the foregoing embodimentsin which the controller is programmed to estimate the ratio. Theembodiments further include variations of the foregoing embodiments inwhich the controller is further programmed to estimate the ratio fromthe stored parameter data.

The embodiments further include variations of the foregoing embodimentsin which the controller is further programmed to estimate the ratioindependently of the stored parameter data. The embodiments furtherinclude variations of the foregoing embodiments in which the controlleris further programmed to estimate the ratio from a signal indicating apump speed, a fluid weight, a flow rate, or a fluid volume. Theembodiments further include variations of the foregoing embodiments inwhich the controller calculates a patient fluid volume or rate of fluidvolume loss from the parameter data.

The embodiments further include variations of the foregoing embodimentsin which the controller calculates the patient fluid volume or rate offluid volume loss from the parameter data, where the parameter dataincludes data responsive to viscosity and a predefined relationshipbetween hematocrit and viscosity wherein the relationship is derivedfrom historical data for the specific patient.

The embodiments further include variations of the foregoing embodimentsin which the controller calculates a rate of fluid volume loss from theparameter data, the parameter data including hematocrit of bloodentering and leaving the treatment device.

The embodiments further include variations of the foregoing embodimentsin which the controller stores a function of hematocrit vs. viscosityand calculates hematocrit responsively to the function. The embodimentsfurther include variations of the foregoing embodiments in which thefunction is a lookup table. The embodiments further include variationsof the foregoing embodiments in which the viscosity sensors includepressure sensors upstream and downstream of a flow restriction. Theembodiments further include variations of the foregoing embodiments inwhich the stored parameter is stored over multiple treatments for thepatient to generate an historical record for the patient.

The embodiments further include variations of the foregoing embodimentsin which the stored parameter over the multiple times is effective toindicate a trend over a course of treatment, the trend being stored overmultiple treatments for the patient to generate an historical record oftrends for the patient.

The embodiments further include variations of the foregoing embodimentsin which the trends are stored as an average or a curve fit to viscosityover time to define a baseline trend which is compared by the controllerto a current trend, and a result of the comparison output as a signal.The embodiments further include variations of the foregoing embodimentsin which the controller is configured to output the parameter data to atreatment log. The embodiments further include variations of theforegoing embodiments in which the pressure sensors include pressurepods.

The embodiments further include variations of the foregoing embodimentsin which the flow restriction is a length of tubing. The embodimentsfurther include variations of the foregoing embodiments in which theviscosity sensor draws a sample of fluid from the fluid circuit andapplies the sample to a viscometer. The embodiments further includevariations of the foregoing embodiments in which the viscosity sensordraws a sample periodically throughout a treatment.

According to another main embodiment, the disclosed subject matterincludes a fluid treatment system with a machine that has one or morepumps and a controller. A fluid circuit is in engagement with the one ormore pumps and having inlet and outlet lines for conveying a fluid toand from a patient respectively. One or more fluid parameter sensors ispresent, at least one of which is in at least the outlet line. The oneor more fluid property sensors generates a fluid property signalindicative of one or more properties of fluid in the at least the outletline. The controller is programmed to sample, at multiple times, thefluid property signal and store parameter data responsive thereto. Thecontroller is further programmed to one of, responsively to the storedparameter data, control the flow of fluid in the inlet and outlet linesor output an estimation of a condition of the machine or the patient andoutput data responsive to the estimation.

The embodiments further include variations of the foregoing embodimentsin which the one or more fluid parameter sensors includes viscositysensors in the inlet and outlet lines. The embodiments further includevariations of the foregoing embodiments in which the inlet line andoutlet lines are blood lines. The embodiments further include variationsof the foregoing embodiments in which the inlet line and outlet linesare peritoneal dialysate lines.

The embodiments further include variations of the foregoing embodimentsin which the inlet and outlet lines are blood lines connected to aninlet and outlet, respectively, of a treatment device. The embodimentsfurther include variations of the foregoing embodiments in which themachine is adapted to control a balance of fluid the patient byregulating a ratio of total volume of fluid removed from the patient tototal volume supplied to the patient.

The embodiments further include variations of the foregoing embodimentsin which the controller is programmed to estimate the ratioindependently of the parameter data. The embodiments further includevariations of the foregoing embodiments in which the controller isfurther programmed to estimate the ratio from the stored parameter data.The embodiments further include variations of the foregoing embodimentsin which the controller is further programmed to estimate the ratioindependently of the stored parameter data.

The embodiments further include variations of the foregoing embodimentsin which the controller is further programmed to estimate the ratio froma signal indicating a pump speed, a fluid weight, a flow rate, or afluid volume. The embodiments further include variations of theforegoing embodiments in which the controller calculates a rate of fluidvolume loss from the parameter data.

The embodiments further include variations of the foregoing embodimentsin which the controller calculates the patient fluid volume or rate offluid volume loss from the parameter data, where the parameter dataincludes data responsive to viscosity and a predefined relationshipbetween hematocrit and viscosity wherein the relationship is derivedfrom historical data for the specific patient.

The embodiments further include variations of the foregoing embodimentsin which the controller stores a function of hematocrit vs. viscosityand calculates hematocrit responsively to the function. The embodimentsfurther include variations of the foregoing embodiments in which thefunction is a lookup table. The embodiments further include variationsof the foregoing embodiments in which the viscosity sensors includepressure sensors upstream and downstream of a flow restriction.

The embodiments further include variations of the foregoing embodimentsin which the stored parameter is stored over multiple treatments for thepatient to generate an historical record for the patient. Theembodiments further include variations of the foregoing embodiments inwhich the stored parameter over the multiple times is effective toindicate a trend over a course of treatment, the trend being stored overmultiple treatments for the patient to generate an historical record oftrends for the patient.

The embodiments further include variations of the foregoing embodimentsin which the trends are stored as an average or a curve fit to viscosityover time to define a baseline trend which is compared by the controllerto a current trend. The embodiments further include variations of theforegoing embodiments in which the controller is configured to outputthe parameter data to a treatment log.

The embodiments further include variations of the foregoing embodimentsin which the one or more fluid parameter sensors include a temperaturesensor, a blood oximeter, an optical hematocrit sensor, a scale adaptedfor measuring the patient's weight, patient heart rate, galvanic skinresistance, and audio and/or video of a treatment area. The embodimentsfurther include variations of the foregoing embodiments in which thecontroller is programmed to implement a classifier whose inputs includethe fluid parameter signal and whose output is applied by the controllerfor display, control, or treatment log.

The embodiments further include variations of the foregoing embodimentsin which the controller is programmed to implement a classifier whoseinputs include the fluid parameter signal and whose output includes asignal indicating at least one of: a diagnosis including a patientinfection and the patient's excess fluid level or weight above dryweight.

The embodiments further include variations of the foregoing embodimentsin which the controller is programmed to automatically change or halt anultrafiltration rate and record a series of the parameter datacorresponding indicating a change in the fluid property over time. Theembodiments further include variations of the foregoing embodiments inwhich the fluid circuit is a blood circuit and the series of parameterdata indicates a change in fluid volume of blood.

The embodiments further include variations of the foregoing embodimentsin which the controller is programmed to automatically change or halt anultrafiltration rate and record a series of the parameter datacorresponding indicating a change in the fluid property over one or moreperiods of time covering periods of different ultrafiltration rates. Theembodiments further include variations of the foregoing embodiments inwhich the fluid circuit is a blood circuit and the series of parameterdata indicates a change in fluid volume of blood.

According to yet another main embodiment, the disclosed subject matterincludes a fluid treatment method that includes pumping fluid through afluid circuit, the pumping conveying fluid to and from a patient for amedical treatment. The method includes detecting viscosity of the fluidfrom the patient. The method further includes sampling the viscositydetected in the detecting, at multiple times during a treatment, andstoring parameter data responsive to viscosity samples resultingtherefrom and controlling a flow of fluid in the fluid circuitresponsively to the stored parameter data.

The embodiments further include variations of the foregoing embodimentsin which the detecting includes detecting viscosity in fluid from thepatient and fluid returned to the patient. The embodiments furtherinclude variations of the foregoing embodiments in which the fluid fromthe patient is blood.

The embodiments further include variations of the foregoing embodimentsin which the fluid from the patient is spent peritoneal dialysate. Theembodiments further include variations of the foregoing embodiments inwhich the fluid circuit conveys the fluid to a treatment device andreturns it to the patient. The embodiments further include variations ofthe foregoing embodiments that include controlling a balance of fluidthe patient by regulating a ratio of total volume of fluid removed fromthe patient to total volume supplied to the patient.

The embodiments further include variations of the foregoing embodimentsthat include estimating the ratio using a controller that controls thepumping. The embodiments further include variations of the foregoingembodiments that include using the controller estimating the ratio fromthe stored parameter data.

The embodiments further include variations of the foregoing embodimentsthat include using the controller estimating the ratio independently ofthe stored parameter data. The embodiments further include variations ofthe foregoing embodiments that include the controller to estimate theratio from a signal indicating a pump speed, a fluid weight, a flowrate, or a fluid volume.

The embodiments further include variations of the foregoing embodimentsthat include, using the controller, calculating a rate of fluid volumeloss from the parameter data. The embodiments further include variationsof the foregoing embodiments that include, using the controller,calculating a rate of fluid volume loss from the parameter data, theparameter data including hematocrit of blood entering and leaving thetreatment device.

The embodiments further include variations of the foregoing embodimentsthat include, using the controller, storing a function of hematocrit vs.viscosity and calculating hematocrit responsively to the function. Theembodiments further include variations of the foregoing embodiments inwhich the function is a lookup table. The embodiments further includevariations of the foregoing embodiments in which the detecting viscosityincludes detecting pressure upstream and downstream of a flowrestriction in the fluid circuit.

The embodiments further include variations of the foregoing embodimentsin which the detecting viscosity includes detecting pressure upstreamand downstream of a flow restriction in the fluid circuit. Theembodiments further include variations of the foregoing embodiments inwhich the storing parameter data includes storing parameter data overmultiple treatments for the patient to generate an historical record forthe patient.

The embodiments further include variations of the foregoing embodimentsin which the stored parameter is effective to indicate a trend over acourse of treatment, the trend being stored over multiple treatments forthe patient to generate an historical record of trends for the patient.

The embodiments further include variations of the foregoing embodimentsin which the trends are stored as an average or a curve fit to viscosityover time to define a baseline trend which is compared by the controllerto a current trend. The embodiments further include variations of theforegoing embodiments in which the controller is configured to outputthe parameter data to a treatment log. The embodiments further includevariations of the foregoing embodiments in which the detecting pressureincludes detecting pressure using a pressure pod.

The embodiments further include variations of the foregoing embodimentsin which the flow restriction includes a length of tubing. Theembodiments further include variations of the foregoing embodiments inwhich the detecting viscosity includes drawing a sample of fluid fromthe fluid circuit and applying the sample to a viscometer. Theembodiments further include variations of the foregoing embodiments inwhich the detecting viscosity includes drawing a sample at regularintervals from the fluid circuit and applying the sample to aviscometer.

According to yet another embodiment, the disclosed subject matterincludes a fluid treatment method that includes pumping fluid through afluid circuit, the pumping conveying fluid to and from a patient for amedical treatment. The method further includes detecting one or morefluid properties of the fluid from a patient to generate a fluidproperty signal. The method further includes using a controller,sampling automatically at multiple times during a treatment, the fluidproperty signal and storing parameter data responsive thereto. Themethod further includes using the controller, responsively to the storedparameter data, controlling the flow of fluid in the inlet and outletlines and outputting an estimation of a condition of the machine or thepatient and output data responsive to the estimation.

The embodiments further include variations of the foregoing embodimentsin which the one or more fluid parameters includes viscosity. Theembodiments further include variations of the foregoing embodiments inwhich the fluid circuit includes blood lines and he fluid from thepatient is blood. The embodiments further include variations of theforegoing embodiments in which the fluid from the patient is spentperitoneal dialysate.

The embodiments further include variations of the foregoing embodimentsin which the fluid circuit conveys the fluid to a treatment device andreturns it to the patient. The embodiments further include variations ofthe foregoing embodiments that include, controlling a balance of fluidthe patient by regulating a ratio of total volume of fluid removed fromthe patient to total volume supplied to the patient.

The embodiments further include variations of the foregoing embodimentsthat include, estimating the ratio using a controller that controls thepumping. The embodiments further include variations of the foregoingembodiments that include, using the controller estimating the ratio fromthe stored parameter data. The embodiments further include variations ofthe foregoing embodiments that include, using the controller estimatingthe ratio independently of the stored parameter data.

The embodiments further include variations of the foregoing embodimentsthat include, the controller to estimate the ratio from a signalindicating a pump speed, a fluid weight, a flow rate, or a fluid volume.The embodiments further include variations of the foregoing embodimentsthat include, using the controller, calculating a rate of fluid volumeloss from the parameter data. The embodiments further include variationsof the foregoing embodiments that include, using the controller,calculating a rate of fluid volume loss from the parameter data, theparameter data including hematocrit of blood entering and leaving thetreatment device. The embodiments further include variations of theforegoing embodiments that include, using the controller, storing afunction of hematocrit vs. viscosity and calculating hematocritresponsively to the function.

The embodiments further include variations of the foregoing embodimentsin which the function is a lookup table. The embodiments further includevariations of the foregoing embodiments in which the detecting viscosityincludes detecting pressure upstream and downstream of a flowrestriction in the fluid circuit.

The embodiments further include variations of the foregoing embodimentsin which the detecting viscosity includes detecting pressure upstreamand downstream of a flow restriction in the fluid circuit. Theembodiments further include variations of the foregoing embodiments inwhich the storing parameter data includes storing parameter data overmultiple treatments for the patient to generate an historical record forthe patient. The embodiments further include variations of the foregoingembodiments in which the stored parameter is effective to indicate atrend over a course of treatment, the trend being stored over multipletreatments for the patient to generate an historical record of trendsfor the patient.

The embodiments further include variations of the foregoing embodimentsin which the trends are stored as an average or a curve fit to viscosityover time to define a baseline trend which is compared by the controllerto a current trend. The embodiments further include variations of theforegoing embodiments in which the controller is configured to outputthe parameter data to a treatment log. The embodiments further includevariations of the foregoing embodiments in which the detecting pressureincludes detecting pressure using a pressure pod.

The embodiments further include variations of the foregoing embodimentsin which the flow restriction includes a length of tubing. Theembodiments further include variations of the foregoing embodiments inwhich the detecting viscosity includes drawing a sample of fluid fromthe fluid circuit and applying the sample to a viscometer.

The embodiments further include variations of the foregoing embodimentsin which the detecting viscosity includes drawing a sample at regularintervals from the fluid circuit and applying the sample to aviscometer.

According to embodiments, the disclosed subject matter further includesa fluid treatment system with a fluid circuit having a control unit anda fluid line adapted for connection to a patient access. First andsecond pressure sensors are positioned along a flow path on either sideof a flow restriction. The control unit is connected to receive samplesof first and second pressure signals, respectively, from the first andsecond pressure sensors. The control unit is programmed to store thesamples to generate at least one dynamic parameter associated with aproperty of a fluid flowing in the fluid line through the flowrestriction. The samples being received and parameter data responsivethereto stored, during a treatment, the at least one dynamic parameterbeing generated responsively to the parameter data. The control unit hasa data store that stores at least one predicted parameter thatcorresponds to an acceptable range of the dynamic parameter. The controlunit programmed further to classify at least one condition of a currenttreatment or at least one condition of a current patient responsively tosaid at least one dynamic parameter and said at least one predictedparameter.

The embodiments further include variations of the foregoing embodimentsin which the at least one dynamic parameter includes a curve-fit to atime series obtained from the samples and the predicted parameterincludes a definition of a curve, the control unit comparing thedefinition with the curve-fit to classify the at least one condition.The embodiments further include variations of the foregoing embodimentsin which the at least one stored parameter includes a curve-fit to atime series obtained from the samples from prior treatments.

The embodiments further include variations of the foregoing embodimentsin which the at least one stored parameter includes a curve-fit to atime series obtained from the samples from prior treatments of a uniquepatient. The embodiments further include variations of the foregoingembodiments in which the at least one stored parameter includes acurve-fit to a time series obtained from the samples from priortreatments.

The embodiments further include variations of the foregoing embodimentsin which the at least one stored parameter includes a curve-fit to atime series obtained from the samples from prior treatments of a uniquepatient. The embodiments further include variations of the foregoingembodiments in which the at least one stored parameter includes acurve-fit to a time series obtained from the samples from priorperitoneal dialysis treatments of a unique class of patients. Theembodiments further include variations of the foregoing embodiments inwhich the at least one stored parameter includes a curve-fit to a timeseries obtained from the samples from prior peritoneal dialysistreatments of multiple patients.

The embodiments further include variations of the foregoing embodimentsin which the at least one stored parameter includes a patient model. Theembodiments further include variations of the foregoing embodiments inwhich the at least one stored parameter includes a patient model, thepatient model including parameters of an individual patient. Theembodiments further include variations of the foregoing embodiments inwhich the parameters of an individual patient include metrics related toobesity.

The embodiments further include variations of the foregoing embodimentsin which the parameters of an individual patient include metrics relatedto a rate of recruitment of factors that affect patient temperature,heat, and viscosity of a fluid from the patient's body.

According to further embodiments, the disclosed subject matter includesa blood treatment system with a blood circuit. The system has a controlunit and a blood line adapted for connection to a patient access. Firstand second pressure sensors are positioned along a flow path on eitherside of a flow restriction. The control unit is connected to receivesamples of first and second pressure signals, respectively, from thefirst and second pressure sensors. The control unit is programmed tostore the samples to generate at least one dynamic parameter associatedwith a property of a blood flowing in the blood line through the flowrestriction. The samples are received and stored, during a treatment,the at least one dynamic parameter being generated responsively tomultiple ones of the samples. The control unit has a data store thatstores at least one predicted parameter that corresponds to anacceptable range of the dynamic parameter. The control unit isprogrammed further to classify at least one condition of a currenttreatment or at least one condition of a current patient responsively tothe at least one dynamic parameter and the at least one predictedparameter.

The embodiments further include variations of the foregoing embodimentsin which the at least one dynamic parameter includes a curve-fit to atime series obtained from the samples and the predicted parameterincludes a definition of a curve, the control unit comparing thedefinition with the curve-fit to classify the at least one condition.

The embodiments further include variations of the foregoing embodimentsin which the at least one stored parameter includes a curve-fit to atime series obtained from the samples from prior treatments. Theembodiments further include variations of the foregoing embodiments inwhich the at least one stored parameter includes a curve-fit to a timeseries obtained from the samples from prior treatments of a uniquepatient. The embodiments further include variations of the foregoingembodiments in which the at least one stored parameter includes acurve-fit to a time series obtained from the samples from priortreatments.

The embodiments further include variations of the foregoing embodimentsin which the at least one stored parameter includes a curve-fit to atime series obtained from the samples from prior treatments of a uniquepatient. The embodiments further include variations of the foregoingembodiments in which the at least one stored parameter includes apatient model. The embodiments further include variations of theforegoing embodiments in which the at least one stored parameterincludes a patient model, the patient model including parameters of anindividual patient.

The embodiments further include variations of the foregoing embodimentsin which the parameters of an individual patient include metrics relatedto obesity. The embodiments further include variations of the foregoingembodiments in which the parameters of an individual patient includemetrics related to a rate of recruitment of factors that affect patienttemperature, heat, and viscosity of a blood from the patient's body.

According to further embodiments, the disclosed subject matter includesa method of treating a patient using a fluid treatment system. Themethod includes using a fluid circuit with a control unit and a fluidline adapted for connection to a patient access. The fluid circuit hasfirst and second pressure sensors positioned along a flow path on eitherside of a flow restriction. The control unit is connected to receivesamples of first and second pressure signals, respectively, from thefirst and second pressure sensors. The method further includes using thecontrol unit, storing the samples to generate at least one dynamicparameter, associated with a property of a fluid flowing in the fluidline through the flow restriction. The method further includes receivingthe samples being received and stored, during a treatment, the at leastone dynamic parameter being generated responsively to multiple ones ofthe samples. The method further includes using the control unit, storingat least one predicted parameter that corresponds to an acceptable rangeof the dynamic parameter in a data store of the control unit. The methodfurther includes using the control unit, classifying at least onecondition of a current treatment or at least one condition of a currentpatient responsively to the at least one dynamic parameter and the atleast one predicted parameter.

The embodiments further include variations of the foregoing embodimentsin which the at least one dynamic parameter includes a curve-fit to atime series obtained from the samples and the predicted parameterincludes a definition of a curve, the control unit comparing thedefinition with the curve-fit to classify the at least one condition.

The embodiments further include variations of the foregoing embodimentsin which the at least one stored parameter includes a curve-fit to atime series obtained from the samples from prior treatments. Theembodiments further include variations of the foregoing embodiments inwhich the at least one stored parameter includes a curve-fit to a timeseries obtained from the samples from prior treatments of a uniquepatient. The embodiments further include variations of the foregoingembodiments in which the at least one stored parameter includes acurve-fit to a time series obtained from the samples from priortreatments.

The embodiments further include variations of the foregoing embodimentsin which the at least one stored parameter includes a curve-fit to atime series obtained from the samples from prior treatments of a uniquepatient. The embodiments further include variations of the foregoingembodiments in which the at least one stored parameter includes acurve-fit to a time series obtained from the samples from priorperitoneal dialysis treatments of a unique class of patients.

The embodiments further include variations of the foregoing embodimentsin which the at least one stored parameter includes a curve-fit to atime series obtained from the samples from prior peritoneal dialysistreatments of multiple patients. The embodiments further includevariations of the foregoing embodiments in which the at least one storedparameter includes a patient model.

The embodiments further include variations of the foregoing embodimentsin which the at least one stored parameter includes a patient model, thepatient model including parameters of an individual patient. Theembodiments further include variations of the foregoing embodiments inwhich the parameters of an individual patient include metrics related toobesity.

The embodiments further include variations of the foregoing embodimentsin which the parameters of an individual patient include metrics relatedto a rate of recruitment of factors that affect patient temperature,heat, and viscosity of a fluid from the patient's body. The embodimentsfurther include variations of any of the foregoing systems or methodswhere a patient condition is estimated by the controller responsively toa combination of two or more of heart rate, hematocrit, blood oxygen,patient weight, galvanic skin resistance, blood or spent peritonealdialysate viscosity, and blood temperature, as detected by a respectivesensor and applied to the controller and a responsive signal generatedby the controller.

The embodiments further include variations of the foregoing embodimentsin which the controller is programmed to implement a classifier toestimate the condition where the classifier has inputs include thecombination and outputs the responsive signal for display, control, ortreatment logging. The embodiments further include variations of theforegoing embodiments in which the condition includes an infection ofthe patient.

Embodiments of the disclosed subject matter include any combination orsubcombination of the limitations of the following dependent claims,that depend from a common independent claim, with the limitations ofthat common independent claim.

Further embodiments include any of the following claims that recite aviscosity-dependent parameter, where the viscosity-dependent parameterincludes a pressure drop indicated by detecting the pressure differentacross a blood circuit element.

It will be appreciated that the modules, processes, systems, andsections described above can be implemented in hardware, hardwareprogrammed by software, software instruction stored on a non-transitorycomputer readable medium or a combination of the above. For example, amethod for determining a patient or treatment condition can beimplemented, for example, using a processor configured to execute asequence of programmed instructions stored on a non-transitory computerreadable medium. For example, the processor can include, but not belimited to, a personal computer or workstation or other such computingsystem that includes a processor, microprocessor, microcontrollerdevice, or is comprised of control logic including integrated circuitssuch as, for example, an Application Specific Integrated Circuit (ASIC).The instructions can be compiled from source code instructions providedin accordance with a programming language such as Java, C++, C#, .net,or the like. The instructions can also comprise code and data objectsprovided in accordance with, for example, the Visual Basic™ language,LabVIEW, or another structured or object-oriented programming language.The sequence of programmed instructions and data associated therewithcan be stored in a non-transitory computer-readable medium such as acomputer memory or storage device which may be any suitable memoryapparatus, such as, but not limited to, read-only memory (ROM),programmable read-only memory (PROM), electrically erasable programmableread-only memory (EEPROM), random-access memory (RAM), flash memory,disk drive, and the like.

Furthermore, the modules, processes, systems, and sections can beimplemented as a single processor or as a distributed processor.Further, it should be appreciated that the steps mentioned above may beperformed on a single or distributed processor (single and/ormulti-core). Also, the processes, modules, and sub-modules described inthe various figures of and for embodiments above may be distributedacross multiple computers or systems or may be co-located in a singleprocessor or system. Exemplary structural embodiment alternativessuitable for implementing the modules, sections, systems, means, orprocesses described herein are provided below.

The modules, processors or systems described above can be implemented asa programmed general purpose computer, an electronic device programmedwith microcode, a hard-wired analog logic circuit, software stored on acomputer-readable medium or signal, an optical computing device, anetworked system of electronic and/or optical devices, a special purposecomputing device, an integrated circuit device, a semiconductor chip,and a software module or object stored on a computer-readable medium orsignal, for example.

Embodiments of the method and system (or their sub-components ormodules), may be implemented on a general-purpose computer, aspecial-purpose computer, a programmed microprocessor or microcontrollerand peripheral integrated circuit element, an ASIC or other integratedcircuit, a digital signal processor, a hardwired electronic or logiccircuit such as a discrete element circuit, a programmed logic circuitsuch as a programmable logic device (PLD), a programmable logic array(PLA), a field-programmable gate array (FPGA), a programmable arraylogic (PAL) device, or the like. In general, any process capable ofimplementing the functions or steps described herein can be used toimplement embodiments of a method, a system, or a computer programproduct (software program stored on a non-transitory computer readablemedium).

Furthermore, embodiments of the disclosed method, system, and computerprogram product may be readily implemented, fully or partially, insoftware using, for example, object or object-oriented softwaredevelopment environments that provide portable source code that can beused on a variety of computer platforms. Alternatively, embodiments ofthe disclosed method, system, and computer program product can beimplemented partially or fully in hardware using, for example, standardlogic circuits or a very-large-scale integration (VLSI) design. Otherhardware or software can be used to implement embodiments depending onthe speed and/or efficiency requirements of the systems, the particularfunction, and/or particular software or hardware system, microprocessor,or microcomputer being utilized. Embodiments of the method, system, andcomputer program product can be implemented in hardware and/or softwareusing any known or later developed systems or structures, devices and/orsoftware by those of ordinary skill in the applicable art from thefunction description provided herein and with a general basic knowledgeof control and measurement systems, machine-assisted diagnosis, and/orcomputer programming arts.

Moreover, embodiments of the disclosed method, system, and computerprogram product can be implemented in software executed on a programmedgeneral-purpose computer, a special purpose computer, a microprocessor,or the like.

The foregoing descriptions apply, in some cases, to examples generatedin a laboratory, but these examples can be extended to productiontechniques. For example, where quantities and techniques apply to thelaboratory examples, they should not be understood as limiting.

Features of the disclosed embodiments may be combined, rearranged,omitted, etc., within the scope of the invention to produce additionalembodiments. Furthermore, certain features may sometimes be used toadvantage without a corresponding use of other features.

FIG. 9 is a block diagram of an example computer system 1000 accordingto an embodiment. In various embodiments, all or parts of system 1000may be included in a medical treatment device/system such as a renalreplacement therapy system. In these embodiments, all or parts of system1000 may provide the functionality of a controller of the medicaltreatment device/systems. In some embodiments, all or parts of system1000 may be implemented as a distributed system, for example, as acloud-based system.

System 1000 includes a computer 1002 such as a personal computer orworkstation or other such computing system that includes a processor1006. However, alternative embodiments may implement more than oneprocessor and/or one or more microprocessors, microcontroller devices,or control logic including integrated circuits such as ASIC.

Computer 1002 further includes a bus 1004 that provides communicationfunctionality among various modules of computer 1002. For example, bus1004 may allow for communicating information/data between processor 1006and a memory 1008 of computer 1002 so that processor 1006 may retrievestored data from memory 1008 and/or execute instructions stored onmemory 1008. In embodiments, such instructions may be compiled fromsource code/objects provided in accordance with a programming languagesuch as Java, C++, C#, .net, Visual Basic™, LabVIEW, or anotherstructured or object-oriented programming language. In embodiments, theinstructions include software modules that, when executed by processor1006, provide renal replacement therapy functionality according to anyof the embodiments disclosed herein.

Memory 1008 may include any volatile or non-volatile computer-readablememory that can be read by computer 1002. For example, memory 1008 mayinclude a non-transitory computer-readable medium such as ROM, PROM,EEPROM, RAM, flash memory, disk drive, etc. Memory 1008 may be aremovable or non-removable medium.

Bus 1004 may further allow for communication between computer 1002 and adisplay 1018, a keyboard 1020, a mouse 1022, and a speaker 1024, eachproviding respective functionality in accordance with variousembodiments disclosed herein, for example, for configuring a treatmentfor a patient and monitoring a patient during a treatment.

Computer 1002 may also implement a communication interface 1010 tocommunicate with a network 1012 to provide any functionality disclosedherein, for example, for alerting a healthcare professional and/orreceiving instructions from a healthcare professional, reportingpatient/device conditions in a distributed system for training a machinelearning algorithm, logging data to a remote repository, etc.Communication interface 1010 may be any such interface known in the artto provide wireless and/or wired communication, such as a network cardor a modem.

Bus 1004 may further allow for communication with a sensor 1014 and/oran actuator 1016, each providing respective functionality in accordancewith various embodiments disclosed herein, for example, for measuringsignals indicative of a patient/device condition and for controlling theoperation of the device accordingly. For example, sensor 1014 mayprovide a signal indicative of a viscosity of a fluid in a fluid circuitin a renal replacement therapy device, and actuator 1016 may operate apump that controls the flow of the fluid responsively to the signals ofsensor 1014.

It is, thus, apparent that there is provided, in accordance with thepresent disclosure, diagnosis based on viscosity changes in treatmentsystems. Many alternatives, modifications, and variations are enabled bythe present disclosure. Features of the disclosed embodiments can becombined, rearranged, omitted, etc., within the scope of the inventionto produce additional embodiments. Furthermore, certain features maysometimes be used to advantage without a corresponding use of otherfeatures. Accordingly, Applicants intend to embrace all suchalternatives, modifications, equivalents, and variations that are withinthe spirit and scope of the present invention.

1. A blood treatment system, comprising: a machine comprising one ormore pumps; a blood circuit in engagement with the one or more pumps andhaving an inlet line and an outlet line for conveying blood to and froma patient, respectively; at least two pressure sensors in at least oneof the inlet line or the outlet line; and a controller configured tocontrol the one or more pumps to convey blood to and from the patient,the controller further configured to use readings of the at least twopressure sensors to determine a change over time in a pressure dropacross a flow restriction in the blood circuit and perform a functionresponsive to the change in the pressure drop, the function comprisingat least one of: generating a notification, adjusting a blood flow ratein the blood circuit, outputting an estimation of a condition of themachine or the patient, or outputting data responsive to saidestimation.
 2. The system of claim 1, wherein the controller isconfigured to sample the pressure drop at multiple times over a periodof time comprising a single treatment or multiple treatments and storedata responsive thereto, wherein the change in the pressure drop isdetermined based on a trend over time of the stored data.
 3. The systemof claim 2, wherein the controller, responsively to said trend,generates a user notification, caution, or alarm, indicating that thetrend is exceeding a limit.
 4. The system of claim 2, wherein the inletline comprises an arterial line and the outlet line comprises a venousline.
 5. The system of claim 2, wherein the inlet line and the outletline are connected to an inlet and an outlet, respectively, of atreatment device configured to treat blood.
 6. The system of claim 2,wherein the machine is adapted to control a balance of fluid in thepatient by regulating a ratio of a total volume of fluid removed fromthe patient to a total volume of fluid supplied to the patient.
 7. Thesystem of claim 6, wherein the controller is programmed to estimate saidratio from said stored data.
 8. The system of claim 6, wherein thecontroller is programmed to estimate said ratio independently of saidstored data by using a signal indicating a pump speed, a fluid weight, aflow rate, or a fluid volume.
 9. The system of claim 2, wherein thecontroller calculates a patient fluid volume or a rate of fluid volumeloss from said stored data.
 10. The system of claim 9, wherein arelationship between viscosity and hematocrit is derived from historicaldata for the patient or for a class of patients corresponding to thepatient.
 11. The system of claim 9, wherein the controller stores afunction of hematocrit versus viscosity-dependent parameter orhemoglobin versus viscosity, wherein the controller calculateshematocrit or hemoglobin responsively to said function and responsivelyto the stored data and wherein the controller outputs said hematocrit orhemoglobin.
 12. The system of claim 11, wherein the function is a lookuptable.
 13. The system of claim 2, wherein the at least one pressuresensor includes two pressure sensors upstream and downstream of the flowrestriction.
 14. The system of claim 2, wherein the stored data isstored over multiple treatments for said patient to generate ahistorical record for said patient.
 15. The system of claim 14, whereinthe stored data over said multiple treatments indicates a trend over acourse of treatment, said trend being stored over the multipletreatments for said patient to generate a historical record of trendsfor said patient.
 16. The system of claim 15, wherein the trends arestored as an average or a curve fit to viscosity over time to provide abaseline trend, wherein the baseline trend is compared by saidcontroller to a current trend, and a result of the comparison is outputas a signal.
 17. The system of claim 2, wherein the controller isconfigured to output said stored data to a treatment log.
 18. The systemof claim 1, wherein the at least one pressure sensor includes pressurepods.
 19. The system of claim 1, wherein the flow restriction is alength of tubing with a predefined shape and inner diameter, acombination of an orifice and a rigid channel, or a precisely sized portwith a shaped inlet and outlet.
 20. The system of claim 1, furthercomprising a viscosity sensor, wherein the viscosity sensor draws asample of blood from said blood circuit and applies the sample to aviscometer. 21-152. (canceled)